OpenRaiser/Intern-Atlas huggingface dataset:
BASS: Boolean Automorphisms Signature Scheme
Venue: arXiv cs.CR 2023
Year: 2023

We offer a digital signature scheme using Boolean automorphisms of a multivariate polynomial algebra over integers. Verification part of this scheme is based on the approximation of the number of zeros of a multivariate Boolean function.
Reversible Adversarial Examples with Beam Search Attack and Grayscale Invariance
Venue: arXiv cs.CR 2023
Year: 2023

Reversible adversarial examples (RAE) combine adversarial attacks and reversible data-hiding technology on a single image to prevent illegal access. Most RAE studies focus on achieving white-box attacks. In this paper, we propose a novel framework to generate reversible adversarial examples, which combines a novel beam search based black-box attack and reversible data hiding with grayscale invariance (RDH-GI). This RAE uses beam search to evaluate the adversarial gain of historical perturbations and guide adversarial perturbations. After the adversarial examples are generated, the framework RDH-GI embeds the secret data that can be recovered losslessly. Experimental results show that our method can achieve an average Peak Signal-to-Noise Ratio (PSNR) of at least 40dB compared to source images with limited query budgets. Our method can also achieve a targeted black-box reversible adversarial attack for the first time.
CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework
Venue: arXiv cs.IR 2023
Year: 2023

Point-of-Interest (POI ) recommendation systems have gained popularity for their unique ability to suggest geographical destinations with the incorporation of contextual information such as time, location, and user-item interaction. Existing recommendation frameworks lack the contextual fusion required for POI systems. This paper presents CAPRI, a novel POI recommendation framework that effectively integrates context-aware models, such as GeoSoCa, LORE, and USG, and introduces a novel strategy for the efficient merging of contextual information. CAPRI integrates an evaluation module that expands the evaluation scope beyond accuracy to include novelty, personalization, diversity, and fairness. With an aim to establish a new industry standard for reproducible results in the realm of POI recommendation systems, we have made CAPRI openly accessible on GitHub, facilitating easy access and contribution to the continued development and refinement of this innovative framework.
Generative Retrieval as Dense Retrieval
Venue: arXiv cs.IR 2023
Year: 2023

Generative retrieval is a promising new neural retrieval paradigm that aims to optimize the retrieval pipeline by performing both indexing and retrieval with a single transformer model. However, this new paradigm faces challenges with updating the index and scaling to large collections. In this paper, we analyze two prominent variants of generative retrieval and show that they can be conceptually viewed as bi-encoders for dense retrieval. Specifically, we analytically demonstrate that the generative retrieval process can be decomposed into dot products between query and document vectors, similar to dense retrieval. This analysis leads us to propose a new variant of generative retrieval, called Tied-Atomic, which addresses the updating and scaling issues by incorporating techniques from dense retrieval. In experiments on two datasets, NQ320k and the full MSMARCO, we confirm that this approach does not reduce retrieval effectiveness while enabling the model to scale to large collections.
Transparency in App Analytics: Analyzing the Collection of User Interaction Data
Venue: arXiv cs.SE 2023
Year: 2023

The rise of mobile apps has brought greater convenience and many options for users. However, many apps use analytics services to collect a wide range of user interaction data, with privacy policies often failing to reveal the types of interaction data collected or the extent of the data collection practices. This lack of transparency potentially breaches data protection laws and also undermines user trust. We conducted an analysis of the top 20 analytic libraries for Android apps to identify common practices of interaction data collection and used this information to develop a standardized collection claim template for summarizing an app's data collection practices wrt. user interaction data. We selected the top 100 apps from popular categories on Google Play and used automatic static analysis to extract collection evidence from their data collection implementations. Our analysis found that a significant majority of these apps actively collected interaction data from UI types such as View (89%), Button (76%), and Textfield (63%), highlighting the pervasiveness of user interaction data collection. By comparing the collection evidence to the claims derived from privacy policy analysis, we manually fact-checked the completeness and accuracy of these claims for the top 10 apps. We found that, except for one app, they all failed to declare all types of interaction data they collect and did not specify some of the collection techniques used.
Prepare the Chair for the Bear! Robot Imagination of Sitting Affordance to Reorient Previously Unseen Chairs
Venue: arXiv cs.RO 2023
Year: 2023

In this letter, a paradigm for the classification and manipulation of previously unseen objects is established and demonstrated through a real example of chairs. We present a novel robot manipulation method, guided by the understanding of object stability, perceptibility, and affordance, which allows the robot to prepare previously unseen and randomly oriented chairs for a teddy bear to sit on. Specifically, the robot encounters an unknown object and first reconstructs a complete 3D model from perceptual data via active and autonomous manipulation. By inserting this model into a physical simulator (i.e., the robot's "imagination"), the robot assesses whether the object is a chair and determines how to reorient it properly to be used, i.e., how to reorient it to an upright and accessible pose. If the object is classified as a chair, the robot reorients the object to this pose and seats the teddy bear onto the chair. The teddy bear is a proxy for an elderly person, hospital patient, or child. Experiment results show that our method achieves a high success rate on the real robot task of chair preparation. Also, it outperforms several baseline methods on the task of upright pose prediction for chairs.
One-to-Many Spectral Upsampling of Reflectances and Transmittances
Venue: arXiv cs.GR 2023
Year: 2023

Spectral rendering is essential for the production of physically-plausible synthetic images, but requires to introduce several changes in the content generation pipeline. In particular, the authoring of spectral material properties (e.g., albedo maps, indices of refraction, transmittance coefficients) raises new problems.While a large panel of computer graphics methods exists to upsample a RGB color to a spectrum, they all provide a one-to-one mapping. This limits the ability to control interesting color changes such as the Usambara effect or metameric spectra. In this work, we introduce a one-to-many mapping in which we show how we can explore the set of all spectra reproducing a given input color. We apply this method to different colour changing effects such as vathochromism -- the change of color with depth, and metamerism.
Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuvers
Venue: arXiv cs.RO 2023
Year: 2023

Developing and testing automated driving models in the real world might be challenging and even dangerous, while simulation can help with this, especially for challenging maneuvers. Deep reinforcement learning (DRL) has the potential to tackle complex decision-making and controlling tasks through learning and interacting with the environment, thus it is suitable for developing automated driving while not being explored in detail yet. This study carried out a comprehensive study by implementing, evaluating, and comparing the two DRL algorithms, Deep Q-networks (DQN) and Trust Region Policy Optimization (TRPO), for training automated driving on the highway-env simulation platform. Effective and customized reward functions were developed and the implemented algorithms were evaluated in terms of onlane accuracy (how well the car drives on the road within the lane), efficiency (how fast the car drives), safety (how likely the car is to crash into obstacles), and comfort (how much the car makes jerks, e.g., suddenly accelerates or brakes). Results show that the TRPO-based models with modified reward functions delivered the best performance in most cases. Furthermore, to train a uniform driving model that can tackle various driving maneuvers besides the specific ones, this study expanded the highway-env and developed an extra customized training environment, namely, ComplexRoads, integrating various driving maneuvers and multiple road scenarios together. Models trained on the designed ComplexRoads environment can adapt well to other driving maneuvers with promising overall performance. Lastly, several functionalities were added to the highway-env to implement this work. The codes are open on GitHub at https://github.com/alaineman/drlcarsim-paper.
UM-CAM: Uncertainty-weighted Multi-resolution Class Activation Maps for Weakly-supervised Fetal Brain Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Accurate segmentation of the fetal brain from Magnetic Resonance Image (MRI) is important for prenatal assessment of fetal development. Although deep learning has shown the potential to achieve this task, it requires a large fine annotated dataset that is difficult to collect. To address this issue, weakly-supervised segmentation methods with image-level labels have gained attention, which are commonly based on class activation maps from a classification network trained with image tags. However, most of these methods suffer from incomplete activation regions, due to the low-resolution localization without detailed boundary cues. To this end, we propose a novel weakly-supervised method with image-level labels based on semantic features and context information exploration. We first propose an Uncertainty-weighted Multi-resolution Class Activation Map (UM-CAM) to generate high-quality pixel-level supervision. Then, we design a Geodesic distance-based Seed Expansion (GSE) method to provide context information for rectifying the ambiguous boundaries of UM-CAM. Extensive experiments on a fetal brain dataset show that our UM-CAM can provide more accurate activation regions with fewer false positive regions than existing CAM variants, and our proposed method outperforms state-of-the-art weakly-supervised methods with image-level labels.
Align, Adapt and Inject: Sound-guided Unified Image Generation
Venue: arXiv cs.GR 2023
Year: 2023

Text-guided image generation has witnessed unprecedented progress due to the development of diffusion models. Beyond text and image, sound is a vital element within the sphere of human perception, offering vivid representations and naturally coinciding with corresponding scenes. Taking advantage of sound therefore presents a promising avenue for exploration within image generation research. However, the relationship between audio and image supervision remains significantly underdeveloped, and the scarcity of related, high-quality datasets brings further obstacles. In this paper, we propose a unified framework 'Align, Adapt, and Inject' (AAI) for sound-guided image generation, editing, and stylization. In particular, our method adapts input sound into a sound token, like an ordinary word, which can plug and play with existing powerful diffusion-based Text-to-Image (T2I) models. Specifically, we first train a multi-modal encoder to align audio representation with the pre-trained textual manifold and visual manifold, respectively. Then, we propose the audio adapter to adapt audio representation into an audio token enriched with specific semantics, which can be injected into a frozen T2I model flexibly. In this way, we are able to extract the dynamic information of varied sounds, while utilizing the formidable capability of existing T2I models to facilitate sound-guided image generation, editing, and stylization in a convenient and cost-effective manner. The experiment results confirm that our proposed AAI outperforms other text and sound-guided state-of-the-art methods. And our aligned multi-modal encoder is also competitive with other approaches in the audio-visual retrieval and audio-text retrieval tasks.
Hallucination is the last thing you need
Venue: arXiv cs.CL 2023
Year: 2023

The legal profession necessitates a multidimensional approach that involves synthesizing an in-depth comprehension of a legal issue with insightful commentary based on personal experience, combined with a comprehensive understanding of pertinent legislation, regulation, and case law, in order to deliver an informed legal solution. The present offering with generative AI presents major obstacles in replicating this, as current models struggle to integrate and navigate such a complex interplay of understanding, experience, and fact-checking procedures. It is noteworthy that where generative AI outputs understanding and experience, which reflect the aggregate of various subjective views on similar topics, this often deflects the model's attention from the crucial legal facts, thereby resulting in hallucination. Hence, this paper delves into the feasibility of three independent LLMs, each focused on understanding, experience, and facts, synthesising as one single ensemble model to effectively counteract the current challenges posed by the existing monolithic generative AI models. We introduce an idea of mutli-length tokenisation to protect key information assets like common law judgements, and finally we interrogate the most advanced publicly available models for legal hallucination, with some interesting results.
Inter-Cell Network Slicing With Transfer Learning Empowered Multi-Agent Deep Reinforcement Learning
Venue: arXiv cs.NI 2023
Year: 2023

Network slicing enables operators to efficiently support diverse applications on a common physical infrastructure. The ever-increasing densification of network deployment leads to complex and non-trivial inter-cell interference, which requires more than inaccurate analytic models to dynamically optimize resource management for network slices. In this paper, we develop a DIRP algorithm with multiple deep reinforcement learning (DRL) agents to cooperatively optimize resource partition in individual cells to fulfill the requirements of each slice, based on two alternative reward functions. Nevertheless, existing DRL approaches usually tie the pretrained model parameters to specific network environments with poor transferability, which raises practical deployment concerns in large-scale mobile networks. Hence, we design a novel transfer learning-aided DIRP (TL-DIRP) algorithm to ease the transfer of DIRP agents across different network environments in terms of sample efficiency, model reproducibility, and algorithm scalability. The TL-DIRP algorithm first centrally trains a generalized model and then transfers the "generalist" to each local agent as "specialist" with distributed finetuning and execution. TL-DIRP consists of two steps: 1) centralized training of a generalized distributed model, 2) transferring the "generalist" to each "specialist" with distributed finetuning and execution. The numerical results show that not only DIRP outperforms existing baseline approaches in terms of faster convergence and higher reward, but more importantly, TL-DIRP significantly improves the service performance, with reduced exploration cost, accelerated convergence rate, and enhanced model reproducibility. As compared to a traffic-aware baseline, TL-DIRP provides about 15% less violation ratio of the quality of service (QoS) for the worst slice service and 8.8% less violation on the average service QoS.
NeRF synthesis with shading guidance
Venue: arXiv cs.CV 2023
Year: 2023

The emerging Neural Radiance Field (NeRF) shows great potential in representing 3D scenes, which can render photo-realistic images from novel view with only sparse views given. However, utilizing NeRF to reconstruct real-world scenes requires images from different viewpoints, which limits its practical application. This problem can be even more pronounced for large scenes. In this paper, we introduce a new task called NeRF synthesis that utilizes the structural content of a NeRF patch exemplar to construct a new radiance field of large size. We propose a two-phase method for synthesizing new scenes that are continuous in geometry and appearance. We also propose a boundary constraint method to synthesize scenes of arbitrary size without artifacts. Specifically, we control the lighting effects of synthesized scenes using shading guidance instead of decoupling the scene. We have demonstrated that our method can generate high-quality results with consistent geometry and appearance, even for scenes with complex lighting. We can also synthesize new scenes on curved surface with arbitrary lighting effects, which enhances the practicality of our proposed NeRF synthesis approach.
Energy-efficient superparamagnetic Ising machine and its application to traveling salesman problems
Venue: arXiv cs.ET 2023
Year: 2023

The growth of artificial intelligence and IoT has created a significant computational load for solving non-deterministic polynomial-time (NP)-hard problems, which are difficult to solve using conventional computers. The Ising computer, based on the Ising model and annealing process, has been highly sought for finding approximate solutions to NP-hard problems by observing the convergence of dynamic spin states. However, it faces several challenges, including high power consumption due to artificial spins and randomness emulated by complex circuits, as well as low scalability caused by the rapidly growing connectivity when considering large-scale problems. Here, we present an experimental Ising annealing computer based on superparamagnetic tunnel junctions (SMTJs) with all-to-all connections, which successfully solves a 70-city travelling salesman problem (4761-node Ising problem). By taking advantage of the intrinsic randomness of SMTJs, implementing a proper global annealing scheme, and using an efficient algorithm, our SMTJ-based Ising annealer shows superior performance in terms of power consumption and energy efficiency compared to other Ising schemes. Additionally, our approach provides a promising way to solve complex problems with limited hardware resources. Moreover, we propose a crossbar array architecture for scalable integration using conventional magnetic random access memories. Our results demonstrate that the SMTJ-based Ising annealing computer with high energy efficiency, speed, and scalability is a strong candidate for future unconventional computing schemes.
The Pricing And Hedging Of Constant Function Market Makers
Venue: arXiv cs.CR 2023
Year: 2023

We investigate the most common type of blockchain-based decentralized exchange, which are known as constant function market makers (CFMMs). We examine the the market microstructure around CFMMs and present a model for valuing the liquidity provider (LP) mechanism and estimating the value of the associated derivatives. We develop a model with two types of traders that have different information and contribute methods for simulating the behavior of each trader and accounting for trade PnL. We also develop ideas around the equilibrium distribution of fair price conditional on the arrival of traders. Finally, we show how these findings might be used to think about parameters for alternative CFMMs.
Allowing Blockchain Loans with Low Collateral
Venue: arXiv cs.CY 2023
Year: 2023

Collateral is an item of value serving as security for the repayment of a loan. In blockchain-based loans, cryptocurrencies serve as the collateral. The high volatility of cryptocurrencies implies a serious barrier of entry with a common practice that collateral values equal multiple times the value of the loan. As assets serving as collateral are locked, this requirement prevents many candidates from obtaining loans. In this paper, we aim to make loans more accessible by offering loans with lower collateral, while keeping the risk for lenders bound. We use a credit score based on data recovered from the blockchain to predict how likely someone is to repay a loan. Our protocol does not risk the initial amount granted by liquidity providers, but only risks part of the interest yield gained by the protocol in the past.
GPU First -- Execution of Legacy CPU Codes on GPUs
Venue: arXiv cs.DC 2023
Year: 2023

Utilizing GPUs is critical for high performance on heterogeneous systems. However, leveraging the full potential of GPUs for accelerating legacy CPU applications can be a challenging task for developers. The porting process requires identifying code regions amenable to acceleration, managing distinct memories, synchronizing host and device execution, and handling library functions that may not be directly executable on the device. This complexity makes it challenging for non-experts to leverage GPUs effectively, or even to start offloading parts of a large legacy application. In this paper, we propose a novel compilation scheme called "GPU First" that automatically compiles legacy CPU applications directly for GPUs without any modification of the application source. Library calls inside the application are either resolved through our partial libc GPU implementation or via automatically generated remote procedure calls to the host. Our approach simplifies the task of identifying code regions amenable to acceleration and enables rapid testing of code modifications on actual GPU hardware in order to guide porting efforts. Our evaluation on two HPC proxy applications with OpenMP CPU and GPU parallelism, four micro benchmarks with originally GPU only parallelism, as well as three benchmarks from the SPEC OMP 2012 suite featuring hand-optimized OpenMP CPU parallelism showcases the simplicity of porting host applications to the GPU. For existing parallel loops, we often match the performance of corresponding manually offloaded kernels, with up to 14.36x speedup on the GPU, validating that our GPU First methodology can effectively guide porting efforts of large legacy applications.
RoTaR: Efficient Row-Based Table Representation Learning via Teacher-Student Training
Venue: arXiv cs.LG 2023
Year: 2023

We propose RoTaR, a row-based table representation learning method, to address the efficiency and scalability issues faced by existing table representation learning methods. The key idea of RoTaR is to generate query-agnostic row representations that could be re-used via query-specific aggregation. In addition to the row-based architecture, we introduce several techniques: cell-aware position embedding, teacher-student training paradigm, and selective backward to improve the performance of RoTaR model.